Job Title

Tech StackModeling & ML Frameworks:Python, scikit-learn, PyTorch, TensorFlow — spanning classicalML, deep learning, and transformer-based architectures. Includes modern ensemble methods(XGBoost, LightGBM) for large-scale structured modeling.Applied Domains: Ranking, Recommendation, Dynamic Pricing, Forecasting, Supply–DemandOptimization, Semantic Search, NLP/NLU, Generative Content SystemsData & Compute: Databricks, PySpark, AWS (S3, Glue, EMR, Athena), ScyllaDB, MongoDB,RedisExperimentation & Optimization: MLflow, Airflow, SageMaker, Bayesian Optimization,Bandit/Sequential ExperimentationLLMs & GenAI: Claude, OpenAI GPT-4, SLMs, LangChain, Cursor IDE, RAG Pipelines,Embedding Models, Vector Search (FAISS / Pinecone)Observability: Grafana, Prometheus, Data Quality Monitors, Custom Model DashboardsWe’re in the early stages of building a Data Science & AI team — the learning curve,innovation velocity, and ownership opportunities are immense. You’ll help define the foundationfor experimentation, production ML pipelines, and GenAI innovation from the ground up.Role : Senior Data Scientist (AI & Data)Location: Remote (Work from Home)We’re hiring a Senior Data Scientist to build the next generation of intelligentdecision systems that power pricing, supply optimization, ranking, and personalizationin our global B2B hotel marketplace.This is a high-impact role at the intersection of machine learning, optimization, andproduct engineering, where you’ll leverage deep statistical modeling and modern MLtechniques to make real-time decisions at scale.You’ll collaborate closely with Product, Engineering, and Data Platform teams tooperationalize data science models that directly improve revenue, conversion, andmarketplace efficiency.You’ll own the full lifecycle of ML models—from experimentation and training todeployment, monitoring, and continuous retraining to ensure performance at scale.Responsibilities● Design and implement ML models for dynamic pricing, availability prediction,and real-time hotel demand optimization.● Develop and maintain data pipelines and feature stores supportinglarge-scale model training and inference.● Leverage Bayesian inference, causal modeling, and reinforcement learning(bandits / sequential decision systems) to drive adaptive decision platforms.● Build ranking / recommendation systems for personalization, relevance, andsupply visibility.● Use LLMs (Claude, GPT-4, SLMs) for:○ Contract parsing, metadata extraction, and mapping resolution○ Semantic search and retrieval-augmented generation (RAG)○ Conversational systems for CRS, rate insights, and partnercommunication○ Automated summarization and content enrichment● Operationalize ML + LLM pipelines on Databricks / AWS for training, inference,and monitoring.● Deploy and monitor models in production with strong observability, tracing,and SLO ownership.● Run A/B experiments and causal validation to measure real business impact.● Collaborate cross-functionally with engineering, data platform, and productteams to translate research into scalable production systems.● Your models will directly influence GMV growth, conversion rates, and partnerrevenue yield across the global marketplace.Requirements● 5–9 years of hands-on experience in Applied ML / Data Science.● Strong proficiency in Python, PySpark, and SQL.● Experience developing models for ranking, pricing, recommendation, orforecasting at scale.● Hands-on with PyTorch or TensorFlow for real-world ML or DL use cases.● Strong grasp of probabilistic modeling, Bayesian methods, and causalinference.● Practical experience integrating LLM/GenAI workflows (LangChain, RAG,embeddings, Claude, GPT, SLMs) into production.● Experience with Databricks, Spark, or SageMaker for distributed training anddeployment.● Familiar with experiment platforms, MLflow, and model observability bestpractices.● Strong business understanding and ability to communicate model impact toproduct stakeholders.Nice to Have● Background in travel-tech, marketplace, or pricing/revenue optimizationdomains.● Experience in retrieval, semantic search, or content-based informationretrieval.● Familiarity with small language model (SLM) optimization for cost-efficientinference.● Prior work on RL/bandit-driven decision systems or personalization engines.● Experience designing AI-assisted developer workflows using tools like Cursor,Claude, or Code Interpreter.